Intelligibility Test Research Articles

Machine Learning-Based Analysis of Digital Movement Assessment and ExerGame Scores for Parkinson's Disease Severity Estimation.

Neurodegenerative Parkinson's Disease (PD) is one of the common incurable diseases among the elderly. Clinical assessments are characterized as standardized means for PD diagnosis. However, relying on medical evaluation of a patient's status can be subjective to physicians' experience, making the assessment process susceptible to human errors. The use of ICT-based tools for capturing the status of patients with PD can provide more objective and quantitative metrics. In this vein, the Personalized Serious Game Suite (PGS) and intelligent Motor Assessment Tests (iMAT), produced within the i-PROGNOSIS European project (www.i-prognosis.eu), are explored in the current study. More specifically, data from 27 patients with PD at Stage 1 (9) and Stage 3 (18) produced from their interaction with PGS/iMAT are analyzed. Five feature vector (FV) scenarios are set, including features from PGS or iMAT scores or their combination, after also taking into consideration the age of patients with PD. These FVs are fed into three machine learning classifiers, i.e., K-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Random Forest (RF), to infer the stage of each patient with PD. A Leave-One-Out Cross-Validation (LOOCV) method is adopted for testing the classification performance. The experimental results show that a high (>90%) classification accuracy is achieved from both data sources (PGS/iMAT), justifying the effectiveness of PGS/iMAT to efficiently reflect the motor skill status of patients with PD and further potentiating PGS/iMAT enhancement with a machine learning a part to infer for the stage of patients with PD. Clearly, this integrated approach provides new opportunities for remote monitoring of the stage of patients with PD, contributing to a more efficient organization and set up of personalized interventions.

Differences between listeners with early and late immersion age in spatial release from masking in various acoustic environments

It is well-known that we benefit from binaural hearing when listening to the speech of interest amongst noise, where spatial cues may release our auditory perception from masking. However, this benefit deteriorates with external factors such as the reverberation in the room, as well as internal factors such as our familiarity with the language of interest. The current study examined spatial release from masking (SRM) experienced by listeners with different age of immersion to New Zealand English (NZE) in varying room acoustics. A speech intelligibility test was conducted using an Ambisonic-based sound reproduction system to reproduce speech and noise as if they were produced in a seminar room and a chapel at two distances between the source and the listener: 2 m and 5 m. The rooms differed in reverberation time (RT), and the distances modified the speech clarity (C50). The participants were split into an early immersed group (n= 20), and a late immersed group (n= 37), where the participants in the early immersed group were immersed in NZE before the age of 13, and those in the late immersed group were immersed after the age of 15. A babble noise was played from eight azimuthal angles (0, ±45∘, ±90∘, ±135∘, 180∘) while the target speech, which was sentences from the Bamford–Kowal–Bench (BKB) corpus, was played from 0∘. Early immersed listeners were able to understand speech better than late immersed listeners within most of the room acoustics tested. However, once the room acoustics caused too adverse listening conditions at a high RT and low C50, neither group could benefit from SRM. The early immersed group was also able to make use of spatial cues to benefit from SRM more than the late immersed group, even in the least reverberant room scenario in the current study. Finally, while room acoustics affected how the groups benefitted from SRM, this effect was only observed when the source was located 5 m from the listener.

An intelligent serological SERS test toward early-stage hepatocellular carcinoma diagnosis through ultrasensitive nanobiosensing

An intelligent serological SERS test toward early-stage hepatocellular carcinoma diagnosis through ultrasensitive nanobiosensing

Benchmarking the Robustness of Object Detection Based on Near-Real Military Scenes

According to the technical requirements of intelligent development of auxiliary combat system, we construct a visual intelligent test platform. A near-real military scene dataset based on physical rendering is built, which contains 11,000 remote sensing images collected by an analog camera taking pictures in different illumination, weather environment, camera shooting angle, and scene scale condition. Besides, we add a natural style transfer module for a single unmodeled military scene image’s multienvironment generation. We conduct experiments to evaluate the stability of several UAV remote sensing image object detection algorithms. Based on the quality and speed value of the tested algorithms, the adaptability scores in different environments are calculated. Furthermore, we propose a comprehensive evaluation index system of military remote object detection based on a hierarchical model. We envision that our comprehensive benchmark will play a role in the evaluation of algorithm capability for military object detection tasks and the improvement of training algorithm capability.

A novel RSG-based intelligent bearing fault diagnosis method for motors in high-noise industrial environment

Bearing fault diagnosis is a critical and challenging task for prognostics and health management of motors. The ability to efficiently and accurately classify the fault categories based on sensor signals is the key to successful bearing fault diagnosis. Although various data-driven methods have been developed for fault diagnosis in recent years, automatic and effective extraction of discriminative fault features from high-noise vibration signals generated in the real-world industrial environment remains a challenging task. To tackle this challenge, this paper proposes a novel deep learning method based on the combination of residual building Unit, soft thresholding and global context, called RSG, to solve the complex mapping relationship between vibration signals and different types of bearing faults. The proposed RSG integrates the working mechanisms of soft threshold and global context to achieve effective noise reduction and feature extraction. A comparative analysis is performed to demonstrate the advantages of the proposed method. Furthermore, the proposed method is tested on a faulty motor dataset collected by our developed intelligent motor test platform based on Industrial Internet of Things. Experimental results show that our method can achieve an average fault diagnosis accuracy of 98%. Thus, the proposed method proves to be an efficient solution for intelligent bearing fault diagnosis for motors in a high-noise industrial environment.

Intelligent high-throughput intervention testing platform in Daphnia.

We present a novel platform for testing the effects of interventions on the life‐ and healthspan of a short‐lived freshwater organism with complex behavior and physiology—the planktonic crustacean Daphnia magna. Within this platform, dozens of complex behavioral features of both routine motion and response to stimuli are continuously quantified over large synchronized cohorts via an automated phenotyping pipeline. We build predictive machine‐learning models calibrated using chronological age and extrapolate onto phenotypic age. We further apply the model to estimate the phenotypic age under pharmacological perturbation. Our platform provides a scalable framework for drug screening and characterization in both life‐long and instant assays as illustrated using a long‐term dose‐response profile of metformin and a short‐term assay of well‐studied substances such as caffeine and alcohol.

Design and Implementation of Online Intelligent Mental Health Testing Platform.

In order to solve the problems of high misevaluation rate and low work efficiency in the process of mental health intelligent evaluation, a method of mental health intelligent evaluation system oriented to the decision tree algorithm is proposed. First, the current research status of mental health intelligent evaluation was analyzed and the framework of mental health intelligent evaluation system was constructed. Then, the mental health intelligent evaluation data were collected and the decision tree algorithm was used to analyze and classify the mental health intelligent evaluation data to obtain the mental health intelligent evaluation results. Finally, specific simulation experiments are used to analyze the feasibility and superiority of the mental health intelligent evaluation system. The experimental results show that the recall rate of each system increases with the increasing number of iterations, and the system has the highest recall rate. Also, it is stable after the number of iterations reaches 20, with good recall and adaptive scheduling performance. The recall rate of comparison system 1 and comparison system 2 fluctuates greatly, and the recall rate is lower than that of the system in this paper. It is proved that the method of the mental health intelligent evaluation system of the decision tree algorithm can effectively solve the problem and improve the accuracy of the mental health intelligent evaluation. The efficiency of mental health intelligent evaluation is improved, and the system stability is better, which can meet the actual requirements of current mental health intelligent evaluation.

Validating Automatic Diadochokinesis Analysis Methods Across Dysarthria Severity and Syllable Task in Amyotrophic Lateral Sclerosis.

Oral diadochokinesis (DDK) is a standard dysarthria assessment task. To extract automatic and semi-automatic DDK measurements, numerous DDK analysis algorithms based on acoustic signal processing are available, including amplitude based, spectral based, and hybrid. However, these algorithms have been predominantly validated in individuals with no perceptible to mild dysarthria. The behavior of these algorithms across dysarthria severity is largely unknown. Likewise, these algorithms have not been tested equally for various syllable types. The goal of this study was to evaluate the performance of five common DDK algorithms as a function of dysarthria severity, considering syllable types. We analyzed 282 DDK recordings of /ba/, /pa/, and /ta/ from 145 participants with amyotrophic lateral sclerosis. Recordings were stratified into mild, moderate, or severe dysarthria groups based on individual performance on the Speech Intelligibility Test. Analysis included manual and automatic estimation of the number of syllables, DDK rate, and cycle-to-cycle temporal variability (cTV). Validation metrics included Bland-Altman mixed-effects limits of agreement between manual and automatic syllable counts, recall and precision between manual and automatic syllable boundary detection, and Kendall's tau-b correlations between manual and algorithm-detected DDK rate and cTV. The amplitude-based algorithm (absolute energy) yielded the strongest correlations with manual analysis across all severity groups for DDK rate (τ b = 0.7-0.84) and cTV (τ b = 0.7-0.84) and the narrowest limits of agreement (-5.92 to 7.12 syllable difference). Moreover, this algorithm also provided the highest mean recall and precision across severity groups for /ba/ and /pa/, but with significantly more variation for/ta/. Algorithms based on signal energy analysis appeared to be the most robust for DDK analysis across dysarthria severity and syllable types; however, it remains prone to error against severe dysarthria and alveolar syllable context. Further development is needed to address this important issue.

Intelligent testing prototype for monitoring thermal variables in a thermal chamber according to optimize an ultrasonic washing machine

The following article shows the mathematical analysis, simulations, design and experimental tests of an intelligent prototype for monitoring the physical variables: temperature, which is measured and recorded from inside a system. It contains a platform in a movement more than 3 degrees of freedom (DOF) that is coupled to 4 springs that allow the mechanical movement of the platform that supports the monitoring system. The recording system has the ability to be inside the thermal system (not outside the data monitoring, which is the usual way) for monitoring the thermal variables regardless of mechanical disturbances of movement that generally physical-chemical processes require it. The complexity of the case is that the monitoring hardware must be inserted into a closed system at the controlled range of work from 25 to 70 degrees Celsius and the composition of 3 DOF adapts the recording system to achieve a robust control for a washing machine that uses ultrasound. Since connection and wiring problems disturb the transfer of information, if the monitoring system is outside the thermal system. For this reason, this designed system has the robustness of hardware to isolate the heat that is produced in the thermal camera with respect to the interior of the recorder. Also the appropriate isolation for the hardware that contains the monitoring system is thermally analyzed, which implies that outside the system the process can be from 25 to 70 degrees Celsius and inside the prototype, where the logger circuitry is located, it is around 25 degrees Celsius. However, the analysis of correct filtering and self-control of the system is also necessary during the processing of the information of registered thermal variables, since a large part of the disturbances for data collection is given by the mechanical movement of two degrees of freedom of the thermal system [1], [2]. Finally, it is suggested for a future improvement of this article, the replacement of traditional (electromechanical) sensors by sensors based on nanostructures, because a large part of solutions and strategies for filtering and control through software can be optimized with robust and fast-response sensors. Moreover, this new sensor can avoid mechanical and electromagnetic disturbances.

Student's Error Analysis In Solving Definite Integral Problem Based On Multiple Intelligences

One of the factors that students perform errors in solving mathematic problem is student’s intelligence. Gardner mentioned there are eight types of multiple intelligences, there are linguistic, logical mathematical, kinesthetic, musical, spatial, interpersonal, intrapersonal and naturalistic. Student’s error can be analyzed using Newman’s error analysis which contains five types of errors, there are reading error, comprehension error, transformation error, process skill error, and encoding error. This research is descriptive qualitative research which aims to describe the types of student’s error in solving definite integral based on multiple intelligences. The subjects of this research are three grade XII senior high school students in Sidoarjo whom have intelligences related to mathematic, there are logical mathematical intelligence, linguistic intelligence and spatial intelligence. Data was collected by giving multiple intelligences test, definite integral problems and interview. Data analysis technique are data reduction, data presentation and data verification. The result of this research showed that: (1) students with logical mathematical intelligence perform transformation error, process skill error and kesalahan encoding error; (2) students with linguistic intelligence perform reading error, comprehension error, transformation error, process skill error and encoding error; (3) students with spatial intelligence perform reading error, transformation error and encoding error.

Picture Description in the Assessment of Connected Speech Intelligibility in Parkinson’s Disease: A Pilot Study

Introduction: Assessment of intelligibility in dysarthria tends to rely on oral reading of sentences or words. However, self-generated utterances are closer to a client's natural speech. This study investigated how transcription of utterances elicited by picture description can be used in the assessment of intelligibility in speakers with Parkinson’s disease. Methods: Speech samples from eleven speakers with Parkinson’s disease and six neurologically healthy persons were audio-recorded. Forty-two naive listeners completed transcriptions of self-generated sentences from a picture description task and orally read sentences from the Swedish Test of Intelligibility, as well as scaled ratings of narrative speech samples. Results: Intelligibility was higher in orally read than self-generated sentences and higher for content words than for the whole sentence in self-generated sentences for most of the speakers, although these within-group differences were not statistically significant at the group level. Adding contextual leads for the listeners increased intelligibility in self-generated utterances significantly but with individual variation. Although correlations between the intelligibility measures were at least moderate or strong, there was a considerable inter- and intra-speaker variability in intelligibility scores between tasks for the speakers with Parkinson’s disease, indicating individual variation of factors that impact intelligibility. Intelligibility scores from neurologically healthy speakers were generally high across tasks with no significant differences between the conditions. Conclusion: Within-speaker variability supports literature recommendations to use multiple methods and tasks when assessing intelligibility. The inclusion of transcription of self-generated utterances elicited by picture description to the intelligibility assessment has the potential to provide additional information to assessment methods based on oral reading of pre-scripted sentences and to inform the planning of interventions.

Research on the Denoising Method of Intelligent Ultrasonic Fuel Consumption Testing in Automobiles

Using ultrasonic time difference method to test automobile fuel consumption, the test accuracy mainly depends on the testing system timing accuracy and ultrasonic flow sensor output signal-to-noise ratio. At present, the timing accuracy of the single-chip can reach the level of picosecond, and the noise mixed in the output signal of the ultrasonic converter is the main factor affecting the accuracy of fuel consumption testing. When the receiving signal contains noise, it will cause the signal amplitude to fluctuate, making the measurement time error. The analysis of same-frequency noise, circuit noise and colored noise is carried out, and the feasible measures to eliminate noise are put forward to provide reference for accurate calculation of sound and development of high-precision automobile fuel consumption test instruments.

Relationships Between Thai EFL Learners’ Factors, Intelligibility, and Comprehensibility Towards Varieties of English

This study aims to investigate intelligibility levels of Thai EFL learners, relationship between intelligibility and learners’ variables, and relationship between intelligibility and comprehensibility scores toward varieties of English. Seventy-five students were recruited as a sample group through stratified random sampling technique. Seven research instruments were used in the study, including a quick placement test, a questionnaire, an intelligibility test, an accent familiarity judgment report, a comprehensibility test, an attitudinal test, and four speech samples produced by speakers of 4 different nationalities. Data were quantitatively analyzed by means of arithmetic mean, standard deviation, and Pearson’s correlation coefficients. The findings revealed that Thai EFL learners received high scores in their intelligibility tests for all speech samples. Thai speaker was rated as the most intelligible to the learners, while American speaker was rated as the least intelligible one. Correlations between intelligibility and other learners’ variables were found only in accent familiarity, language proficiency, and attitude. A significant correlation was also found between intelligibility and comprehensibility scores towards varieties of English of Thai EFL learners.

Analysis and Research of Intelligent Test and Appraise System Based on ADO. NET

This paper expounds the research and development of ADO.NET object model, VB.NET development environment and other related technologies and intelligent test and appraise system, analyzes the practical significance of system research and development and use, discusses the research and development process of database setting, array application, random test, intelligent test and appraise, information interaction, score analysis and other functions, and carries out system practice test, function upgrade and application prospect analysis. The system has been continuously used in the practical training of specialized courses. The results show that the system has good practicability and is well received by teachers and students. It is worth upgrading, perfecting and popularizing.

Intelligent Body Measurement System (IBMS) in Colleges and Universities Based on Internet of Things(IOT) Technology

Nowadays, teenagers' physical quality is getting worse and worse. Therefore, the Party Central Committee has established the educational policy of "health first" as the guiding ideology. In order to fully implement the party's education policy, a wave of sports towards stadiums or playgrounds has swept across the country. However, schools lack effective management means and methods for large-scale sunshine sports. Therefore, many colleges and universities have put forward tough measures for students' middle and long-distance running in sunshine sports, requiring each student to complete a certain number of kilometers of long-distance running distance every semester, record students' long-distance running with manual recording method, and finally count it into students' total score as a course. This scheme provides a good entry point for the development of sunshine sports, but the disadvantages of manual recording method also appear: a large number of students, a large number of long-distance runs, a large amount of data, inconvenient statistics, and recording with notes, low efficiency, heavy workload of statisticians, and error prone. Therefore, this paper puts forward the design of College intelligent physical testing system based on IOT technology, and develops the college intelligent physical testing system. This paper discusses the design objectives and principles of the ibms and the overall design idea of the system software, and takes a university as an example to test the packet loss rate of the intelligent test system under the IOT technology and the body temperature index under different distances. The experimental results show that the packet loss rate is very low and almost negligible, and in terms of communication distance, there is no packet loss within 500m; The website is stable, the information is uploaded in time, the calculation accuracy is high, and the students' feedback is good; When the distance is 1m, the body temperature is 36.6℃, when the distance is 12M, the body temperature is 36.5℃, and the error is small. The system has good flexibility. The hardware test shows that the hardware equipment selection of this system is perfect, the function is perfect, and the stability and flexibility are very high.

A novel model-independent data augmentation method for fault diagnosis in smart manufacturing

With the rapid development of information technology, data-driven fault diagnosis has gained more and more attention because it provides a new way for enterprises to save costs. Considering that there are few abnormalities in equipment operation in actual industrial applications, it is still a challenge to implement data-driven fault diagnosis that requires a large amount of fault data. To tackle the challenge, this paper proposes a model-independent data augmentation method, which is a weighted combination of the two time series data augmentation methods, i.e. Gaussian noise and signal stretching. The experimental dataset is collected from an intelligent motor test platform. The fault diagnosis model based on support vector machine and feedforward neural network are applied to study the ability of the proposed data augmentation method in terms of model independence. Experimental results show that the proposed data augmentation methods can significantly improve the accuracy of fault diagnosis.

Automatic parking trajectory tracking control strategy on LQR and PID

This study selects two degree of freedom dynamic model as the research object, and the automatic parking process is analyzed. The longitudinal cascade controller of speed PID and position PID is used to control the longitudinal motion of the vehicle. The lateral tracking controller based on LQR optimal theory is adopted, and the feedforward control is added to form the optimal feedforward LQR controller as the lateral tracking controller. Based on the co-simulation platform of MATLAB/Simulink and CarSim, the function of the designed automatic parking trajectory tracking control strategy is verified. The car test was completed. The ultrasonic sensor, raspberry pie and Arduino were used as the main parts to build an intelligent car test platform to simulate the performance of the designed tracking control strategy in the real vehicle. Software simulation results and vehicle test results showed that the designed control strategy can ensure the path tracking performance of vehicle under normal parking speed, and has good tracking accuracy, real-time performance and vehicle driving stability.

Image Deep Learning Assisted Prediction of Mechanical and Corrosion Behavior for Al-Zn-Mg Alloys

The use of metallographic images to predict the mechanical properties of materials and their corrosion behavior is helpful in achieving nondestructive detection and quality control. However, after a long-term attempt, the traditional methods cannot accurately correlate the mechanical properties and corrosion behavior of materials with the corresponding microstructure images. In this study, we propose a deep learning strategy to predict the mechanical property and corrosion behavior of large-scale extruded aluminum profiles using surface optical microstructure images. The proposed models with remarkable properties were established through experimental dataset collection, dataset preprocessing, deep learning network modification, and key parameter screening. Taking extruded Al-Zn-Mg alloys with different surface microstructures as example materials, 4,800 sets of “metallographic image – hardness (HV) – corrosion potential (E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">corr</sub> )” data were experimentally collected to establish the HV and E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">corr</sub> models with prediction accuracies of 90% and 82%, respectively. The proposed HV and E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">corr</sub> models exhibit great generalization ability with mean average errors of 1.8 HV and 7.0 mV on experimental validation sets, respectively. The proposed model can accurately correlate the metallographic images, mechanical property, and corrosion behavior, which can provide theoretical support for intelligent and nondestructive testing methods to further prevent unexpected material failure.

Updating sentences lists for assessment speech perception.

Adapt a list of sentences for a speech intelligibility test. A speech material data base consisting of 200 phonetically balanced sentences was analyzed and partially updated. In the first stage, 60 reviewers, specialists in linguistics and speech and hearing science, analyzed the sentences in relation to the parameters of familiarity, meaning and predictability using an on-line questionnaire. Cronbach's Alpha coefficient was used to analyze the internal consistency of the questionnaire. In the second stage, the reviewers analyzed whether they were in accordance with the criteria indicated by the literature for the construction of sentences. In the first stage, the responses of 15 reviewers who completed the entire questionnaire were analyzed. Agreement between reviewers was high for all criteria. 71 sentences were recommended for modification in the first stage, with predictability being the most indicated parameter as requiring change. In the second stage, 28 more sentences were selected for adjustment, with the presence of a proper name in the sentence being the most frequently cited criterion. It was possible to adapt a list of sentences in order to provide speech language therapists with a free of charge speech perception protocol. It is hoped that this new test can assist in standardizing assessment for normal hearing adults and individuals with hearing loss in Brazilian Portuguese.

Optimized Vehicle Durability Testing by Means of an Intelligent Test Driver Guidance System

Vehicle manufacturers use special test courses which must be endured damage-free by the vehicle prototype until a compulsory mileage is reached. The driving instructions of this durability road test cannot always be implemented in a reproducible manner, for example due to the influence of driver, weather conditions or route closures. Furthermore, a test course based on many years of experience can only be slowly adapted to new trends in customer usage behavior.This article presents a new type of driver guidance system which, depending on the loads already endured, indicates the test driver the optimal driving instructions to reach a customer-oriented load target. The route calculation is dynamically adapted according to the actually generated and measured loads. For this purpose, the system accesses the relevant in-vehicle sensor data and combines it with road segments of a digital map. In this way, a database of the loads which were achieved on each road segment, is filled. This information is used as input of a heuristic algorithm which determines the best combination of road segments ahead for reaching the load target. As the load database is updated with every road segment driven on during the test, a learning process takes place that continuously adapts the route determination to driving style and changed road conditions. Thus, by monitoring the measured loads and by using the artificial intelligence of the driver guidance system, the customer-oriented load target of the durability road test is optimally achieved resulting in a high testing quality.